初始化项目，由ModelHub XC社区提供模型

Model: lzumot/MODULARMOJO_Mistral_V1
Source: Original Platform

README.md

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+---
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- finetuned
+inference:
+  parameters:
+    temperature: 0.01
+---
+
+A Mistral7B Instruct (https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) 
+Finetune using QLoRA on the docs available in https://docs.modular.com/mojo/
+
+The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
+
+## Instruction format
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+import torch
+
+device = "cuda" # the device to load the model onto
+
+model_name = "mcysqrd/MODULARMOJO_Mistral_V1"
+model = AutoModelForCausalLM.from_pretrained(model_name, 
+                                             use_flash_attention_2=True,
+                                             max_memory={0: "24GB"},
+                                             device_map="auto", 
+                                             trust_remote_code=True,
+                                             low_cpu_mem_usage=True,
+                                             return_dict=True,
+                                             torch_dtype=torch.bfloat16,
+                                            )
+                                            
+tokenizer = AutoTokenizer.from_pretrained(model_name,add_bos_token=True,trust_remote_code=True)
+
+model.config.use_cache = True
+def stream(user_prompt):
+    runtimeFlag = "cuda:0"
+    system_prompt = 'MODULAR_MOJO'
+    B_INST, E_INST = "[INST]", "[/INST]"
+    prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n{E_INST}"
+    inputs = tokenizer([prompt], return_tensors="pt").to(runtimeFlag)
+    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+    _ = model.generate(**inputs, streamer=streamer, max_new_tokens=1600)
+
+stream("""can you translate this python code to mojo to make more performant making T as struct?
+    class T():
+        self.init(v:float):
+            self.value=v
+    
+    def sum_objects(a:T,b:T)->T:
+        return T(a.v+b.v)""")
+```